Upload wikisql_VALUE.py

wikisql_VALUE.py

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+"""A large crowd-sourced dataset for developing natural language interfaces for relational databases"""
+
+
+import json
+import os
+
+import datasets
+
+
+_CITATION = """\
+@article{zhongSeq2SQL2017,
+  author    = {Victor Zhong and
+               Caiming Xiong and
+               Richard Socher},
+  title     = {Seq2SQL: Generating Structured Queries from Natural Language using
+               Reinforcement Learning},
+  journal   = {CoRR},
+  volume    = {abs/1709.00103},
+  year      = {2017}
+}
+"""
+
+_DESCRIPTION = """\
+A large crowd-sourced dataset for developing natural language interfaces for relational databases
+"""
+
+_DATA_URL = "https://huggingface.co/datasets/SALT-NLP/wikisql_VALUE/resolve/main/data.zip"
+
+_AGG_OPS = ["", "MAX", "MIN", "COUNT", "SUM", "AVG"]
+_COND_OPS = ["=", ">", "<", "OP"]
+
+
+class WikiSQL(datasets.GeneratorBasedBuilder):
+    """WikiSQL: A large crowd-sourced dataset for developing natural language interfaces for relational databases"""
+
+    VERSION = datasets.Version("0.1.0")
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "phase": datasets.Value("int32"),
+                    "question": datasets.Value("string"),
+                    "table": {
+                        "header": datasets.features.Sequence(datasets.Value("string")),
+                        "page_title": datasets.Value("string"),
+                        "page_id": datasets.Value("string"),
+                        "types": datasets.features.Sequence(datasets.Value("string")),
+                        "id": datasets.Value("string"),
+                        "section_title": datasets.Value("string"),
+                        "caption": datasets.Value("string"),
+                        "rows": datasets.features.Sequence(datasets.features.Sequence(datasets.Value("string"))),
+                        "name": datasets.Value("string"),
+                    },
+                    "sql": {
+                        "human_readable": datasets.Value("string"),
+                        "sel": datasets.Value("int32"),
+                        "agg": datasets.Value("int32"),
+                        "conds": datasets.features.Sequence(
+                            {
+                                "column_index": datasets.Value("int32"),
+                                "operator_index": datasets.Value("int32"),
+                                "condition": datasets.Value("string"),
+                            }
+                        ),
+                    },
+                }
+            ),
+            # If there's a common (input, target) tuple from the features,
+            # specify them here. They'll be used if as_supervised=True in
+            # builder.as_dataset.
+            supervised_keys=None,
+            # Homepage of the dataset for documentation
+            homepage="https://github.com/salesforce/WikiSQL",
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        """Returns SplitGenerators."""
+        dl_dir = dl_manager.download_and_extract(_DATA_URL)
+        dl_dir = os.path.join(dl_dir, "data")
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.AppE.TEST,
+                gen_kwargs={
+                    "main_filepath": os.path.join(dl_dir, "test_AppE.jsonl"),
+                    "tables_filepath": os.path.join(dl_dir, "test.tables.jsonl"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.AppE.VALIDATION,
+                gen_kwargs={
+                    "main_filepath": os.path.join(dl_dir, "dev_AppE.jsonl"),
+                    "tables_filepath": os.path.join(dl_dir, "dev.tables.jsonl"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.AppE.TRAIN,
+                gen_kwargs={
+                    "main_filepath": os.path.join(dl_dir, "train_AppE.jsonl"),
+                    "tables_filepath": os.path.join(dl_dir, "train.tables.jsonl"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.ChcE.TEST,
+                gen_kwargs={
+                    "main_filepath": os.path.join(dl_dir, "test_ChcE.jsonl"),
+                    "tables_filepath": os.path.join(dl_dir, "test.tables.jsonl"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.ChcE.VALIDATION,
+                gen_kwargs={
+                    "main_filepath": os.path.join(dl_dir, "dev_ChcE.jsonl"),
+                    "tables_filepath": os.path.join(dl_dir, "dev.tables.jsonl"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.ChcE.TRAIN,
+                gen_kwargs={
+                    "main_filepath": os.path.join(dl_dir, "train_ChcE.jsonl"),
+                    "tables_filepath": os.path.join(dl_dir, "train.tables.jsonl"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.CollSgE.TEST,
+                gen_kwargs={
+                    "main_filepath": os.path.join(dl_dir, "test_CollSgE.jsonl"),
+                    "tables_filepath": os.path.join(dl_dir, "test.tables.jsonl"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.CollSgE.VALIDATION,
+                gen_kwargs={
+                    "main_filepath": os.path.join(dl_dir, "dev_CollSgE.jsonl"),
+                    "tables_filepath": os.path.join(dl_dir, "dev.tables.jsonl"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.CollSgE.TRAIN,
+                gen_kwargs={
+                    "main_filepath": os.path.join(dl_dir, "train_CollSgE.jsonl"),
+                    "tables_filepath": os.path.join(dl_dir, "train.tables.jsonl"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.SAE.TEST,
+                gen_kwargs={
+                    "main_filepath": os.path.join(dl_dir, "test.jsonl"),
+                    "tables_filepath": os.path.join(dl_dir, "test.tables.jsonl"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.SAE.VALIDATION,
+                gen_kwargs={
+                    "main_filepath": os.path.join(dl_dir, "dev.jsonl"),
+                    "tables_filepath": os.path.join(dl_dir, "dev.tables.jsonl"),
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.SAE.TRAIN,
+                gen_kwargs={
+                    "main_filepath": os.path.join(dl_dir, "train.jsonl"),
+                    "tables_filepath": os.path.join(dl_dir, "train.tables.jsonl"),
+                },
+            ),
+        ]
+
+    def _convert_to_human_readable(self, sel, agg, columns, conditions):
+        """Make SQL query string. Based on https://github.com/salesforce/WikiSQL/blob/c2ed4f9b22db1cc2721805d53e6e76e07e2ccbdc/lib/query.py#L10"""
+
+        rep = f"SELECT {_AGG_OPS[agg]} {columns[sel] if columns is not None else f'col{sel}'} FROM table"
+
+        if conditions:
+            rep += " WHERE " + " AND ".join([f"{columns[i]} {_COND_OPS[o]} {v}" for i, o, v in conditions])
+        return " ".join(rep.split())
+
+    def _generate_examples(self, main_filepath, tables_filepath):
+        """Yields examples."""
+
+        # Build dictionary to table_ids:tables
+        with open(tables_filepath, encoding="utf-8") as f:
+            tables = [json.loads(line) for line in f]
+            id_to_tables = {x["id"]: x for x in tables}
+
+        with open(main_filepath, encoding="utf-8") as f:
+            for idx, line in enumerate(f):
+                row = json.loads(line)
+                row["table"] = id_to_tables[row["table_id"]]
+                del row["table_id"]
+
+                # Handle missing data
+                row["table"]["page_title"] = row["table"].get("page_title", "")
+                row["table"]["section_title"] = row["table"].get("section_title", "")
+                row["table"]["caption"] = row["table"].get("caption", "")
+                row["table"]["name"] = row["table"].get("name", "")
+                row["table"]["page_id"] = str(row["table"].get("page_id", ""))
+
+                # Fix row types
+                row["table"]["rows"] = [[str(e) for e in r] for r in row["table"]["rows"]]
+
+                # Get human-readable version
+                row["sql"]["human_readable"] = self._convert_to_human_readable(
+                    row["sql"]["sel"],
+                    row["sql"]["agg"],
+                    row["table"]["header"],
+                    row["sql"]["conds"],
+                )
+
+                # Restructure sql->conds
+                # - wikiSQL provides a tuple [column_index, operator_index, condition]
+                #   as 'condition' can have 2 types (float or str) we convert to dict
+                for i in range(len(row["sql"]["conds"])):
+                    row["sql"]["conds"][i] = {
+                        "column_index": row["sql"]["conds"][i][0],
+                        "operator_index": row["sql"]["conds"][i][1],
+                        "condition": str(row["sql"]["conds"][i][2]),
+                    }
+                yield idx, row